5 citations · 5 across the 3 of their papers we have counts for
6 papers
TextualRL: Textual Reinforcement Learning for Context Management
Tianyu Huai, Jie Zhou, Bo Mao +3
LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, turning execution rewards into r…
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution
Hang He, Linting Wang, Guanming Liu +14
Scaling interactive environments is important for training long-horizon agents through reinforcement learning. These environments often require further testing and refinement after…
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
Bo Mao, Hang He, Linting Wang +17
Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic interacti…
Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents
Bo Mao, Jie Zhou, Yutao Yang +5
Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learning agents for long-horizon tas…
Transforming Monolithic Foundation Models into Embodied Multi-Agent Architectures for Human-Robot Collaboration
Nan Sun, Bo Mao, Yongchang Li +3
Foundation models have become central to unifying perception and planning in robotics, yet real-world deployment exposes a mismatch between their monolithic assumption that a singl…
AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment
Nan Sun, Bo Mao, Yongchang Li +2
Current service robots suffer from limited natural language communication abilities, heavy reliance on predefined commands, ongoing human intervention, and, most notably, a lack of…